Overview

Dataset statistics

Number of variables14
Number of observations1039
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory113.8 KiB
Average record size in memory112.1 B

Variable types

Numeric13
Categorical1

Alerts

Number words female is highly correlated with Total words and 2 other fieldsHigh correlation
Total words is highly correlated with Number words female and 5 other fieldsHigh correlation
Number of words lead is highly correlated with Number words female and 3 other fieldsHigh correlation
Difference in words lead and co-lead is highly correlated with Total words and 3 other fieldsHigh correlation
Number of male actors is highly correlated with Total words and 2 other fieldsHigh correlation
Number of female actors is highly correlated with Number words female and 1 other fieldsHigh correlation
Number words male is highly correlated with Total words and 3 other fieldsHigh correlation
Mean Age Female is highly correlated with Mean Age Male and 1 other fieldsHigh correlation
Age Co-Lead is highly correlated with Mean Age Male and 1 other fieldsHigh correlation
Mean Age Male is highly correlated with Mean Age Female and 2 other fieldsHigh correlation
Age Lead is highly correlated with Mean Age MaleHigh correlation
Number words female has 21 (2.0%) zeros Zeros

Reproduction

Analysis started2022-11-18 22:46:56.655985
Analysis finished2022-11-18 22:47:38.268438
Duration41.61 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Number words female
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct895
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2334.256015
Minimum0
Maximum17658
Zeros21
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:38.401295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile237.7
Q1904
median1711
Q33030.5
95-th percentile6930.5
Maximum17658
Range17658
Interquartile range (IQR)2126.5

Descriptive statistics

Standard deviation2157.216744
Coefficient of variation (CV)0.9241560179
Kurtosis6.342849518
Mean2334.256015
Median Absolute Deviation (MAD)967
Skewness2.123789262
Sum2425292
Variance4653584.081
MonotonicityNot monotonic
2022-11-18T23:47:38.579633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
021
 
2.0%
16884
 
0.4%
18583
 
0.3%
11543
 
0.3%
8643
 
0.3%
20943
 
0.3%
11383
 
0.3%
8323
 
0.3%
9723
 
0.3%
11203
 
0.3%
Other values (885)990
95.3%
ValueCountFrequency (%)
021
2.0%
1021
 
0.1%
1031
 
0.1%
1041
 
0.1%
1051
 
0.1%
1101
 
0.1%
1111
 
0.1%
1211
 
0.1%
1221
 
0.1%
1241
 
0.1%
ValueCountFrequency (%)
176581
0.1%
135301
0.1%
130541
0.1%
125961
0.1%
122261
0.1%
121081
0.1%
120021
0.1%
114081
0.1%
106881
0.1%
105821
0.1%

Total words
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1008
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11004.36862
Minimum1351
Maximum67548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:38.772634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1351
5-th percentile3541.4
Q16353.5
median9147
Q313966.5
95-th percentile23771.6
Maximum67548
Range66197
Interquartile range (IQR)7613

Descriptive statistics

Standard deviation6817.397413
Coefficient of variation (CV)0.6195173613
Kurtosis7.996606653
Mean11004.36862
Median Absolute Deviation (MAD)3402
Skewness1.993266526
Sum11433539
Variance46476907.48
MonotonicityNot monotonic
2022-11-18T23:47:38.929709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70743
 
0.3%
86113
 
0.3%
81742
 
0.2%
139562
 
0.2%
188642
 
0.2%
27502
 
0.2%
122862
 
0.2%
134862
 
0.2%
57132
 
0.2%
62942
 
0.2%
Other values (998)1017
97.9%
ValueCountFrequency (%)
13511
0.1%
13681
0.1%
13711
0.1%
14681
0.1%
15221
0.1%
16581
0.1%
16721
0.1%
17261
0.1%
18701
0.1%
19541
0.1%
ValueCountFrequency (%)
675481
0.1%
575241
0.1%
437681
0.1%
412601
0.1%
409281
0.1%
390381
0.1%
377781
0.1%
336361
0.1%
334021
0.1%
325381
0.1%

Number of words lead
Real number (ℝ≥0)

HIGH CORRELATION

Distinct964
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4108.256978
Minimum318
Maximum28102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:39.117727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum318
5-th percentile1057.3
Q12077
median3297
Q35227
95-th percentile9738.8
Maximum28102
Range27784
Interquartile range (IQR)3150

Descriptive statistics

Standard deviation2981.251156
Coefficient of variation (CV)0.7256729976
Kurtosis10.26313284
Mean4108.256978
Median Absolute Deviation (MAD)1463
Skewness2.301127511
Sum4268479
Variance8887858.455
MonotonicityNot monotonic
2022-11-18T23:47:39.276597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24983
 
0.3%
39763
 
0.3%
7893
 
0.3%
18942
 
0.2%
43562
 
0.2%
28072
 
0.2%
13432
 
0.2%
21352
 
0.2%
28912
 
0.2%
18752
 
0.2%
Other values (954)1016
97.8%
ValueCountFrequency (%)
3181
0.1%
4721
0.1%
5011
0.1%
5061
0.1%
5291
0.1%
5511
0.1%
5731
0.1%
5891
0.1%
6111
0.1%
6411
0.1%
ValueCountFrequency (%)
281021
0.1%
267981
0.1%
243761
0.1%
198921
0.1%
161481
0.1%
157081
0.1%
147941
0.1%
146421
0.1%
144901
0.1%
142141
0.1%

Difference in words lead and co-lead
Real number (ℝ≥0)

HIGH CORRELATION

Distinct951
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2525.024062
Minimum1
Maximum25822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:39.432032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile165.8
Q1814.5
median1834
Q33364
95-th percentile7654.4
Maximum25822
Range25821
Interquartile range (IQR)2549.5

Descriptive statistics

Standard deviation2498.747279
Coefficient of variation (CV)0.9895934527
Kurtosis12.76299788
Mean2525.024062
Median Absolute Deviation (MAD)1184
Skewness2.620408806
Sum2623500
Variance6243737.966
MonotonicityNot monotonic
2022-11-18T23:47:39.584112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6944
 
0.4%
6224
 
0.4%
32003
 
0.3%
5973
 
0.3%
3523
 
0.3%
5193
 
0.3%
5033
 
0.3%
34763
 
0.3%
7923
 
0.3%
15402
 
0.2%
Other values (941)1008
97.0%
ValueCountFrequency (%)
11
0.1%
41
0.1%
111
0.1%
131
0.1%
141
0.1%
151
0.1%
161
0.1%
171
0.1%
231
0.1%
241
0.1%
ValueCountFrequency (%)
258221
0.1%
191381
0.1%
181821
0.1%
178221
0.1%
136921
0.1%
129211
0.1%
120601
0.1%
114361
0.1%
112741
0.1%
110581
0.1%

Number of male actors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct27
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.767083734
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:40.310144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q310
95-th percentile15
Maximum29
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.901439172
Coefficient of variation (CV)0.5023042503
Kurtosis3.006479544
Mean7.767083734
Median Absolute Deviation (MAD)2
Skewness1.222559422
Sum8070
Variance15.22122761
MonotonicityNot monotonic
2022-11-18T23:47:40.508143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
6120
11.5%
5115
11.1%
7111
10.7%
4111
10.7%
8109
10.5%
988
8.5%
1078
7.5%
1157
 
5.5%
352
 
5.0%
1243
 
4.1%
Other values (17)155
14.9%
ValueCountFrequency (%)
114
 
1.3%
230
 
2.9%
352
5.0%
4111
10.7%
5115
11.1%
6120
11.5%
7111
10.7%
8109
10.5%
988
8.5%
1078
7.5%
ValueCountFrequency (%)
291
 
0.1%
281
 
0.1%
271
 
0.1%
262
 
0.2%
231
 
0.1%
222
 
0.2%
213
0.3%
202
 
0.2%
191
 
0.1%
186
0.6%

Year
Real number (ℝ≥0)

Distinct51
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1999.862368
Minimum1939
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:40.744632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile1982
Q11994
median2000
Q32009
95-th percentile2013
Maximum2015
Range76
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.40663225
Coefficient of variation (CV)0.005203674222
Kurtosis3.537650153
Mean1999.862368
Median Absolute Deviation (MAD)7
Skewness-1.267261948
Sum2077857
Variance108.2979948
MonotonicityNot monotonic
2022-11-18T23:47:41.001631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200962
 
6.0%
201058
 
5.6%
199955
 
5.3%
199747
 
4.5%
200045
 
4.3%
201144
 
4.2%
199841
 
3.9%
200237
 
3.6%
200137
 
3.6%
200836
 
3.5%
Other values (41)577
55.5%
ValueCountFrequency (%)
19392
0.2%
19491
 
0.1%
19542
0.2%
19582
0.2%
19592
0.2%
19601
 
0.1%
19682
0.2%
19723
0.3%
19734
0.4%
19742
0.2%
ValueCountFrequency (%)
201519
 
1.8%
201424
 
2.3%
201331
3.0%
201227
2.6%
201144
4.2%
201058
5.6%
200962
6.0%
200836
3.5%
200732
3.1%
200622
 
2.1%

Number of female actors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.507218479
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:41.231630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.08852629
Coefficient of variation (CV)0.5954936375
Kurtosis2.238918326
Mean3.507218479
Median Absolute Deviation (MAD)1
Skewness1.221849332
Sum3644
Variance4.361942063
MonotonicityNot monotonic
2022-11-18T23:47:41.420633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2227
21.8%
3208
20.0%
4184
17.7%
1159
15.3%
598
9.4%
671
 
6.8%
739
 
3.8%
826
 
2.5%
912
 
1.2%
107
 
0.7%
Other values (3)8
 
0.8%
ValueCountFrequency (%)
1159
15.3%
2227
21.8%
3208
20.0%
4184
17.7%
598
9.4%
671
 
6.8%
739
 
3.8%
826
 
2.5%
912
 
1.2%
107
 
0.7%
ValueCountFrequency (%)
161
 
0.1%
123
 
0.3%
114
 
0.4%
107
 
0.7%
912
 
1.2%
826
 
2.5%
739
 
3.8%
671
 
6.8%
598
9.4%
4184
17.7%

Number words male
Real number (ℝ≥0)

HIGH CORRELATION

Distinct960
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4561.85563
Minimum0
Maximum31146
Zeros5
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:41.652632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile867.9
Q12139.5
median3824
Q35887.5
95-th percentile10755.2
Maximum31146
Range31146
Interquartile range (IQR)3748

Descriptive statistics

Standard deviation3417.855987
Coefficient of variation (CV)0.7492249348
Kurtosis7.549387365
Mean4561.85563
Median Absolute Deviation (MAD)1840
Skewness2.026475006
Sum4739768
Variance11681739.55
MonotonicityNot monotonic
2022-11-18T23:47:41.894051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.5%
34683
 
0.3%
31143
 
0.3%
18583
 
0.3%
32993
 
0.3%
11712
 
0.2%
58362
 
0.2%
45522
 
0.2%
1562
 
0.2%
22402
 
0.2%
Other values (950)1012
97.4%
ValueCountFrequency (%)
05
0.5%
1131
 
0.1%
1141
 
0.1%
1301
 
0.1%
1562
 
0.2%
1861
 
0.1%
2041
 
0.1%
2251
 
0.1%
2321
 
0.1%
2421
 
0.1%
ValueCountFrequency (%)
311461
0.1%
256281
0.1%
226501
0.1%
224221
0.1%
204641
0.1%
200441
0.1%
184961
0.1%
177921
0.1%
171281
0.1%
169561
0.1%

Gross
Real number (ℝ≥0)

Distinct317
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.1491819
Minimum0
Maximum1798
Zeros10
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:42.146495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q122
median60
Q3143.5
95-th percentile374.2
Maximum1798
Range1798
Interquartile range (IQR)121.5

Descriptive statistics

Standard deviation151.7615507
Coefficient of variation (CV)1.365386125
Kurtosis24.05202261
Mean111.1491819
Median Absolute Deviation (MAD)48
Skewness3.786103854
Sum115484
Variance23031.56828
MonotonicityNot monotonic
2022-11-18T23:47:42.444557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127
 
2.6%
224
 
2.3%
818
 
1.7%
717
 
1.6%
1114
 
1.3%
3414
 
1.3%
3213
 
1.3%
613
 
1.3%
512
 
1.2%
412
 
1.2%
Other values (307)875
84.2%
ValueCountFrequency (%)
010
 
1.0%
127
2.6%
224
2.3%
39
 
0.9%
412
1.2%
512
1.2%
613
1.3%
717
1.6%
818
1.7%
96
 
0.6%
ValueCountFrequency (%)
17981
0.1%
12491
0.1%
11031
0.1%
9371
0.1%
8821
0.1%
8802
0.2%
8531
0.1%
8441
0.1%
8391
0.1%
8131
0.1%

Mean Age Male
Real number (ℝ≥0)

HIGH CORRELATION

Distinct542
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.35376582
Minimum19
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:42.631948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile28.8
Q137.48076923
median42.6
Q347.33333333
95-th percentile54.72857143
Maximum71
Range52
Interquartile range (IQR)9.852564103

Descriptive statistics

Standard deviation7.81710979
Coefficient of variation (CV)0.1845670541
Kurtosis0.2906314251
Mean42.35376582
Median Absolute Deviation (MAD)4.971428571
Skewness0.05257910605
Sum44005.56269
Variance61.10720546
MonotonicityNot monotonic
2022-11-18T23:47:42.805952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4220
 
1.9%
4314
 
1.3%
4012
 
1.2%
41.511
 
1.1%
4610
 
1.0%
4810
 
1.0%
389
 
0.9%
378
 
0.8%
368
 
0.8%
45.666666678
 
0.8%
Other values (532)929
89.4%
ValueCountFrequency (%)
191
0.1%
20.666666671
0.1%
211
0.1%
221
0.1%
231
0.1%
23.51
0.1%
23.751
0.1%
242
0.2%
24.583333331
0.1%
24.62
0.2%
ValueCountFrequency (%)
711
0.1%
701
0.1%
691
0.1%
67.51
0.1%
66.333333331
0.1%
661
0.1%
631
0.1%
62.333333331
0.1%
62.166666671
0.1%
621
0.1%

Mean Age Female
Real number (ℝ≥0)

HIGH CORRELATION

Distinct274
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.92958774
Minimum11
Maximum81.33333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:42.972947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile23.33333333
Q129.5
median35
Q341.5
95-th percentile51.35
Maximum81.33333333
Range70.33333333
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.957192996
Coefficient of variation (CV)0.2492985185
Kurtosis1.306353151
Mean35.92958774
Median Absolute Deviation (MAD)6
Skewness0.7384501139
Sum37330.84167
Variance80.23130636
MonotonicityNot monotonic
2022-11-18T23:47:43.149954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3731
 
3.0%
3528
 
2.7%
3025
 
2.4%
2925
 
2.4%
2825
 
2.4%
3424
 
2.3%
3322
 
2.1%
3221
 
2.0%
3121
 
2.0%
2721
 
2.0%
Other values (264)796
76.6%
ValueCountFrequency (%)
111
0.1%
132
0.2%
162
0.2%
172
0.2%
182
0.2%
18.666666671
0.1%
192
0.2%
19.251
0.1%
19.666666672
0.2%
202
0.2%
ValueCountFrequency (%)
81.333333331
 
0.1%
711
 
0.1%
70.51
 
0.1%
691
 
0.1%
67.51
 
0.1%
66.333333331
 
0.1%
652
0.2%
633
0.3%
62.666666671
 
0.1%
62.51
 
0.1%

Age Lead
Real number (ℝ≥0)

HIGH CORRELATION

Distinct68
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.71607315
Minimum11
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:43.366996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20
Q130
median38
Q346
95-th percentile62
Maximum81
Range70
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.28590219
Coefficient of variation (CV)0.3173333758
Kurtosis0.2450172233
Mean38.71607315
Median Absolute Deviation (MAD)8
Skewness0.5248594002
Sum40226
Variance150.9433927
MonotonicityNot monotonic
2022-11-18T23:47:43.548386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3242
 
4.0%
3640
 
3.8%
2939
 
3.8%
3339
 
3.8%
3839
 
3.8%
4338
 
3.7%
3438
 
3.7%
4136
 
3.5%
4236
 
3.5%
3034
 
3.3%
Other values (58)658
63.3%
ValueCountFrequency (%)
113
 
0.3%
123
 
0.3%
131
 
0.1%
144
 
0.4%
152
 
0.2%
164
 
0.4%
177
0.7%
1810
1.0%
198
0.8%
2012
1.2%
ValueCountFrequency (%)
811
 
0.1%
801
 
0.1%
782
0.2%
771
 
0.1%
763
0.3%
751
 
0.1%
741
 
0.1%
722
0.2%
711
 
0.1%
704
0.4%

Age Co-Lead
Real number (ℝ≥0)

HIGH CORRELATION

Distinct73
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.48604427
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2022-11-18T23:47:43.732387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20
Q128
median34
Q341
95-th percentile60
Maximum85
Range78
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.04669574
Coefficient of variation (CV)0.3394769969
Kurtosis1.497207445
Mean35.48604427
Median Absolute Deviation (MAD)7
Skewness0.9967891749
Sum36870
Variance145.1228783
MonotonicityNot monotonic
2022-11-18T23:47:43.912387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3456
 
5.4%
2854
 
5.2%
3148
 
4.6%
3242
 
4.0%
2741
 
3.9%
2940
 
3.8%
3540
 
3.8%
3739
 
3.8%
3037
 
3.6%
3936
 
3.5%
Other values (63)606
58.3%
ValueCountFrequency (%)
71
 
0.1%
81
 
0.1%
92
0.2%
103
0.3%
112
0.2%
124
0.4%
132
0.2%
144
0.4%
152
0.2%
163
0.3%
ValueCountFrequency (%)
851
 
0.1%
841
 
0.1%
801
 
0.1%
792
0.2%
772
0.2%
751
 
0.1%
744
0.4%
721
 
0.1%
712
0.2%
702
0.2%

Lead
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size8.2 KiB
Male
785 
Female
254 

Length

Max length6
Median length4
Mean length4.488931665
Min length4

Characters and Unicode

Total characters4664
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male785
75.6%
Female254
 
24.4%

Length

2022-11-18T23:47:44.110386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-18T23:47:44.281181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
male785
75.6%
female254
 
24.4%

Most occurring characters

ValueCountFrequency (%)
e1293
27.7%
a1039
22.3%
l1039
22.3%
M785
16.8%
F254
 
5.4%
m254
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3625
77.7%
Uppercase Letter1039
 
22.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1293
35.7%
a1039
28.7%
l1039
28.7%
m254
 
7.0%
Uppercase Letter
ValueCountFrequency (%)
M785
75.6%
F254
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin4664
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1293
27.7%
a1039
22.3%
l1039
22.3%
M785
16.8%
F254
 
5.4%
m254
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII4664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1293
27.7%
a1039
22.3%
l1039
22.3%
M785
16.8%
F254
 
5.4%
m254
 
5.4%

Interactions

2022-11-18T23:47:35.375102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:06.487688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:09.065001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:11.907356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:14.933709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:17.012309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.425948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:21.884111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.444648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.623591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:28.748701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:31.123020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.406585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:35.515093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:06.819192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:09.291913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:12.200354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:15.165712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:17.172304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.575496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:22.131114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.590132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.742666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:29.004703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:31.362021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.537586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:35.653093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:07.051483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:09.512798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:13.145662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:15.344707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:17.423306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.712452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:22.333116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.730131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.862671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:29.245167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:31.645020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.692990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:35.776094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:07.243483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:09.720876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:13.272662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:15.487707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:17.652722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.851492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:22.548399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.853204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.992746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:29.463332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:31.830020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.836991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:35.908093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:07.460484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:09.913559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:13.462661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:15.634707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:17.916719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.985668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:22.740808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.987192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:27.114069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:29.687330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:31.976020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.976989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:36.037152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:07.670333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:10.100555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:13.646702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:15.773708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:18.184926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:20.195674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:22.918682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:25.137278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:27.237267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:29.867668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:32.115024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:34.119990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:36.208637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:07.836331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:10.358474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:13.807697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:15.917707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:18.420290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:20.366091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:23.107683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:25.289078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:27.368267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.032668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:32.277021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:34.276281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:36.450127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:08.054675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:10.652496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:13.951702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:16.076861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:18.600290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:20.554093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:23.301209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:25.463078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:27.487267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.205668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:32.472020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:34.465094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:36.715127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:08.243773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:10.854346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:14.111408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:16.224221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:18.752289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:20.816541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:23.512630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:25.645078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:27.606524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.376667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:32.630535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:34.633093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:36.957130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:08.398769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:11.066348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:14.251413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:16.384222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:18.878930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:21.062063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:23.708148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.122591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:27.855528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.537020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:32.765537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:34.781094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:37.175127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:08.527770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:11.245346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:14.399707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:16.534221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.004947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:21.267065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:23.864147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.237604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:28.044367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.669019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:32.939213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:34.922093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:37.379126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:08.667770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:11.460347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:14.559707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:16.691204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.128949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:21.490034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.039148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.372592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:28.190256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.801019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.122114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:35.084093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:37.575127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:08.811867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:11.631353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:14.760707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:16.845569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:19.266948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:21.686689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:24.267224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:26.498592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:28.491675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:30.946019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:33.265263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-18T23:47:35.228093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-18T23:47:44.403521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-18T23:47:44.629250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-18T23:47:44.944364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-18T23:47:45.354162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-18T23:47:45.696984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-18T23:47:37.832552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-18T23:47:38.143438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Number words femaleTotal wordsNumber of words leadDifference in words lead and co-leadNumber of male actorsYearNumber of female actorsNumber words maleGrossMean Age MaleMean Age FemaleAge LeadAge Co-LeadLead
0151263942251.03432199552631142.051.50000042.33333346.065.0Female
1152487802020.01219920014523637.039.12500029.33333358.034.0Male
21554176942.07877196813079376.042.50000037.00000046.037.0Male
3107398553440.026231220022534219.035.22222221.50000033.023.0Male
4131776883835.03149819884253640.045.25000045.00000036.039.0Male
5149258721491.099411199742889327.045.90909136.50000055.041.0Male
6150053221191.02876198032631269.047.00000024.50000061.025.0Male
734960982692.02472919882305753.043.00000031.00000048.031.0Male
885788514042.034761320012395289.047.41666728.50000033.027.0Male
9261996261604.08699197365403565.026.50000022.00000020.026.0Male

Last rows

Number words femaleTotal wordsNumber of words leadDifference in words lead and co-leadNumber of male actorsYearNumber of female actorsNumber words maleGrossMean Age MaleMean Age FemaleAge LeadAge Co-LeadLead
102999939511146.0147520101180674.049.00000035.00000047.035.0Male
1030174880362065.06561020132422378.050.33333325.00000033.036.0Male
1031227693513422.022099201433653200.049.88888934.33333345.032.0Male
10328991986974.0288219952113214.061.50000046.00000042.061.0Male
103312717261232.01105319811367194.048.66666733.00000054.033.0Male
103430323981334.01166519732761174.043.20000031.00000046.024.0Male
103563284041952.01876199225820172.037.16666724.00000021.034.0Female
103613262750877.035622000354753.027.50000027.66666728.025.0Male
10374623994775.052819963275732.042.85714338.50000029.032.0Female
10382735119463410.015361320074580132.044.09090950.00000038.048.0Male